MM and NHL Trends

SETUP & DATA WRANGLING

Open libraries

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knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(survival)
library(survminer)
library(ggsurvfit)
library(ggplot2)
library(haven)
library(readxl)
library(lubridate)
library(zipcodeR)
library(gtsummary)
library(gt)

Read in data

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db = read_excel(path="keep11_2.xlsx")

insurance = read_excel(path="inscvgpr.xlsx")

db <- db %>%
  mutate(
    rxdate = ymd(rxdate),
    Tx.year = year(rxdate),
    Fiscal.year = case_when(
      rxdate >= ymd("2017-07-01") & rxdate <= ymd("2018-06-30") ~ 2018,
      rxdate >=ymd("2018-07-01") & rxdate <= ymd("2019-06-30") ~ 2019,
      rxdate >=ymd("2019-07-01") & rxdate <= ymd("2020-06-30") ~ 2020,
      rxdate >=ymd("2020-07-01") & rxdate <= ymd("2021-06-30") ~ 2021,
      rxdate >=ymd("2021-07-01") & rxdate <= ymd("2022-06-30") ~ 2022,
      rxdate >=ymd("2022-07-01") & rxdate <= ymd("2023-06-30") ~ 2023,
      rxdate >=ymd("2022-07-01") & rxdate <= ymd("2024-06-30") ~ 2024
    ),
    Distance.to.FH = zip_distance("98115",zipcode, lonlat = TRUE, units = "miles")$distance,
    tx = as.integer(tx)
  )

Zip.df <- reverse_zipcode(db$zipcode) %>% unique() # Read in using reverse_zipcode function in zipcodeR package


db <- 
  left_join(
    by = "zipcode",
    x = db,
    y = Zip.df %>% select(zipcode, median_household_income, median_home_value, population, population_density)
  )

db <-
  left_join(
    by = "upn",
    x = db,
    y = insurance %>% select(PAYOR_NAME, FINANCIAL_CLASS, upn)
  )

CHANGES OVER TIME

Number of procedures (i.e., auto, allo, Abecma, Carvykti, Breyanzi, Yescarta)

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db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  count(Fiscal.year) %>%
  ggplot + aes(Fiscal.year, n) + 
  geom_point(stat="identity") + 
  geom_line(stat="identity", color="blue") + 
  ylab("Total number of procedures") + 
  xlab(NULL) +
  geom_text(aes(label=n), position=position_stack(vjust=1.05)) + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c( 2018,2019, 2020, 2021, 2022,2023)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Plasma cell disorders: Bar graph of auto, allo, Abecma, and Carvykti

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## Counts
db %>%
  filter(dx.category == "MM") %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  count(rxtype, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    total = sum(n),
    pct = prop.table(n) * 100,
    Procedure = factor(rxtype, levels = c("Abecma","Carvykti", "Allo", "Auto"))
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Procedure) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
  geom_smooth(aes(x = Fiscal.year, y=total), method = "lm", se = FALSE, color = "#104a8e", inherit.aes = FALSE, linetype = "dashed", size = 0.5)+
  ggtitle("Distribution of procedures - Multiple Myeloma") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140, 160, 180)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Lymphomas: Bar graph of auto, allo, Breyanzi, and Yescarta

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## Counts
db %>%
  filter(dx.category == "NHL") %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  count(rxtype, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    total = sum(n),
    pct = prop.table(n) * 100,
    Procedure = factor(rxtype, levels = c("Breyanzi","Yescarta", "Allo", "Auto"))
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Procedure) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
  geom_smooth(aes(x = Fiscal.year, y=total), method = "lm", se = FALSE, color = "#104a8e", inherit.aes = FALSE, linetype = "dashed", size = 0.5)+
  ggtitle("Distribution of procedures - Lymphoma") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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NHL and MM: transplant vs CAR-T

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  mutate(
    Procedure = case_when(
      rxtype == "Auto" ~ "Transplant",
      rxtype == "Allo" ~ "Transplant",
      rxtype == "Carvykti" ~ "CAR-T",
      rxtype == "Abecma" ~ "CAR-T",
      rxtype == "Yescarta" ~ "CAR-T",
      rxtype == "Breyanzi" ~ "CAR-T"
    )
  ) %>%
  count(Procedure, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    total = sum(n),
    pct = prop.table(n) * 100,
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Procedure) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
  ggtitle("CAR-T vs Transplant") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Bar graph of 1st vs 2nd ASCTs

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  filter(dx.category == "MM") %>%
  filter(rxtype == "Auto") %>%
  count(rxnum, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    ASCT = factor(recode(rxnum, "1" = "1st", "2" = "2nd", "3" = "3rd"), levels = c("3rd", "2nd", "1st") )
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = ASCT) + 
  geom_bar(stat="identity") + 
  ylab("Number of ASCTs") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  ggtitle("1st vs 2nd ASCT") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140, 160, 180)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Bar graph of inpatient vs outpatient ASCTs

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  filter(rxtype == "Auto" & dx.category == "MM") %>%
  count(inpt, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Setting = factor(ifelse(inpt=="Y","Elective admission","Outpatient"), levels = c("Outpatient","Elective admission"))
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Setting) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n)), position=position_stack(vjust=0.5)) + 
  ggtitle("Inpatient vs outpatient ASCT (n)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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## Proportion
db %>%
  filter(rxtype == "Auto" & dx.category == "MM") %>%
  count(inpt, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Setting = factor(ifelse(inpt=="Y","Elective admission","Outpatient"), levels = c("Outpatient","Elective admission"))
    ) %>%
  ggplot + aes(Fiscal.year, pct, fill = Setting) + 
  geom_bar(stat="identity") + 
  ylab("Proportion of patients (%)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) + 
  ggtitle("Inpatient vs outpatient ASCT (%)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(20, 40, 60, 80, 100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Bar graph of total inpatient duration

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db %>%
  group_by(Fiscal.year) %>%
  summarise(Inpatient.duration = median(inday, na.rm=TRUE), sd = sd(inday, na.rm = TRUE), n=n() ) %>%
  ggplot + aes(Fiscal.year, Inpatient.duration) + 
  geom_bar(stat="identity") + 
  geom_errorbar(aes(x=Fiscal.year, ymin = Inpatient.duration- (sd/sqrt(n) ), ymax=Inpatient.duration+(sd/sqrt(n) ) ), width=0.4, color = "black", alpha=0.9) +
  ylab("Inpatient duration, days (median)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(Inpatient.duration)), position=position_stack(vjust=0.5), color = "white") + 
  ggtitle("Total inpatient duration") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(2, 4, 6, 8, 10, 12, 14) ) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Bar graph of distance to FH

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db %>%
  group_by(Fiscal.year) %>%
  summarise(Distance = median(Distance.to.FH, na.rm = TRUE), sd = sd(Distance.to.FH, na.rm = TRUE), n=n()) %>%
  ggplot + aes(Fiscal.year, Distance) + 
  geom_bar(stat="identity") + 
  #geom_errorbar(aes(x=Fiscal.year, ymin = Distance- (sd/sqrt(n) ), ymax=Distance+(sd/sqrt(n) ) ), width=0.4, color = "orange", alpha=0.9) +
  ylab("Distance to FHCC, miles (median)") + 
  xlab(NULL) +
  geom_text(aes(label=signif(Distance,3)), position=position_stack(vjust=0.5), color = "white") + 
  ggtitle("Distance to FHCC") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  #scale_y_continuous(breaks = c(2, 4, 6, 8, 10, 12, 14) ) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Bar graph of ages

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  mutate(
    Age.category = case_when(
      rxage <50 ~ "<50",
      rxage >=50 & rxage <55 ~ "50-55",
      rxage >=55 & rxage <60 ~ "55-60",
      rxage >=60 & rxage <65 ~ "60-65",
      rxage >=65 ~ "\u226565",
      .default = "other"
    )
  ) %>%
  count(Age.category, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Age) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of age (n)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300, 350, 400)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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## Proportion
db %>%
  mutate(
    Age.category = case_when(
      rxage <50 ~ "<50",
      rxage >=50 & rxage <55 ~ "50-55",
      rxage >=55 & rxage <60 ~ "55-60",
      rxage >=60 & rxage <65 ~ "60-65",
      rxage >=65 ~ "\u226565",
      .default = "other"
    )
  ) %>%
  count(Age.category, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
    ) %>%
  ggplot + aes(Fiscal.year, pct, fill = Age) + 
  geom_bar(stat="identity") + 
  ylab("Proportion of patients (%)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of age (%)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(20,40,60,80,100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Bar graph of ages, filtered on MM

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  filter(dx.category == "MM") %>%
  mutate(
    Age.category = case_when(
      rxage <50 ~ "<50",
      rxage >=50 & rxage <55 ~ "50-55",
      rxage >=55 & rxage <60 ~ "55-60",
      rxage >=60 & rxage <65 ~ "60-65",
      rxage >=65 ~ "\u226565",
      .default = "other"
    )
  ) %>%
  count(Age.category, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Age) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of age (n)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300, 350, 400)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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## Proportion
db %>%
  filter(dx.category == "MM") %>%
  mutate(
    Age.category = case_when(
      rxage <50 ~ "<50",
      rxage >=50 & rxage <55 ~ "50-55",
      rxage >=55 & rxage <60 ~ "55-60",
      rxage >=60 & rxage <65 ~ "60-65",
      rxage >=65 ~ "\u226565",
      .default = "other"
    )
  ) %>%
  count(Age.category, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
    ) %>%
  ggplot + aes(Fiscal.year, pct, fill = Age) + 
  geom_bar(stat="identity") + 
  ylab("Proportion of patients (%)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of age (%)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20,40,60,80,100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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Bar graph of sex

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## Counts
db %>%
  filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
  count(sex, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Sex = sex
    ) %>%
  ggplot + aes(Fiscal.year, n, fill = Sex) + 
  geom_bar(stat="identity") + 
  ylab("Number of patients") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n,sprintf("\n(%1.1f", pct),"%)")), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of sex (n)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2019, 2020, 2021, 2022, 2023)) +
  scale_y_continuous(breaks = c(20,40,60,80,100, 120, 140, 160)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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## Proportion
db %>%
  count(sex, Fiscal.year) %>%
  group_by(Fiscal.year) %>%
  mutate(
    pct = prop.table(n) * 100,
    Sex = sex
    ) %>%
  ggplot + aes(Fiscal.year, pct, fill = Sex) + 
  geom_bar(stat="identity") + 
  ylab("Proportion of patients (%)") + 
  xlab(NULL) +
  geom_text(aes(label=paste0(n,sprintf("\n(%1.1f", pct),"%)")), position=position_stack(vjust=0.5)) + 
  ggtitle("Distribution of sex (%)") + 
  theme_classic() + 
  scale_fill_brewer(palette = "Pastel1") + 
  scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
  scale_y_continuous(breaks = c(20,40,60,80,100)) +
  theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))

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TABLES OF REFERRALS AND INSURANCE

Table of referring facilities

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theme_gtsummary_compact()

db %>%
  transmute(
    "Rx year" = Fiscal.year,
    "Referring facility" = referring.facility
  ) %>%
  tbl_summary(
    by = "Rx year",
    missing = "ifany",
    sort = all_categorical() ~ "frequency",
    statistic = list(
      all_continuous() ~ "{median} ({p25}, {p75})",
      all_categorical() ~ "{n} ({p}%)"
    )
  ) %>%
  bold_labels()

<<<<<<< HEAD

<<<<<<< HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622 <<<<<<< HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622 <<<<<<< HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622 <<<<<<< HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622 <<<<<<< HEAD =======

Characteristic	2018, N = 831	2019, N = 2221	2020, N = 2061	2021, N = 2231	2022, N = 2831	2023, N = 3211	2024, N = 1331	2018, N = 1921	2019, N = 2281	2020, N = 1981	2021, N = 2551	2022, N = 2901	2023, N = 3081
Referring facility
Internal	22 (39%)	39 (24%)	35 (22%)	35 (21%)	56 (23%)	67 (24%)	35 (35%)
Other	11 (19%)	29 (18%)	26 (16%)	29 (18%)	34 (14%)	40 (14%)	9 (8.9%)
Multicare	2 (3.5%)	6 (3.7%)	16 (10%)	6 (3.6%)	14 (5.9%)	23 (8.1%)	5 (5.0%)
Northwest Medical Specialties	1 (1.8%)	9 (5.6%)	14 (8.9%)	4 (2.4%)	11 (4.6%)	14 (4.9%)	7 (6.9%)
Providence	2 (3.5%)	4 (2.5%)	6 (3.8%)	8 (4.8%)	14 (5.9%)	22 (7.7%)	4 (4.0%)
Valley Med Center	4 (7.0%)	8 (4.9%)	5 (3.2%)	6 (3.6%)	5 (2.1%)	13 (4.6%)	6 (5.9%)
Swedish	2 (3.5%)	7 (4.3%)	4 (2.5%)	3 (1.8%)	11 (4.6%)	4 (1.4%)	1 (1.0%)
Confluence Health	1 (1.8%)	8 (4.9%)	3 (1.9%)	5 (3.0%)	7 (2.9%)	4 (1.4%)	2 (2.0%)
Kadlec Clinic	1 (1.8%)	3 (1.9%)	5 (3.2%)	8 (4.8%)	6 (2.5%)	3 (1.1%)	4 (4.0%)
Virginia Mason	1 (1.8%)	2 (1.2%)	1 (0.6%)	7 (4.2%)	11 (4.6%)	6 (2.1%)	1 (1.0%)
Cancer Care Northwest	1 (1.8%)	2 (1.2%)	2 (1.3%)	1 (0.6%)	11 (4.6%)	10 (3.5%)	1 (1.0%)
Alaska Oncology & Hematology	0 (0%)	2 (1.2%)	0 (0%)	11 (6.7%)	3 (1.3%)	8 (2.8%)	1 (1.0%)
Peace Health	0 (0%)	2 (1.2%)	4 (2.5%)	6 (3.6%)	4 (1.7%)	8 (2.8%)	1 (1.0%)
Everett Clinic	0 (0%)	0 (0%)	8 (5.1%)	3 (1.8%)	5 (2.1%)	7 (2.5%)	1 (1.0%)
Skagit	1 (1.8%)	2 (1.2%)	4 (2.5%)	2 (1.2%)	6 (2.5%)	8 (2.8%)	1 (1.0%)
Kootenai Cancer Center	0 (0%)	1 (0.6%)	5 (3.2%)	7 (4.2%)	4 (1.7%)	2 (0.7%)	3 (3.0%)
Straub Clinic and Hospital	0 (0%)	4 (2.5%)	5 (3.2%)	1 (0.6%)	6 (2.5%)	5 (1.8%)	1 (1.0%)
Vista Oncology	0 (0%)	5 (3.1%)	0 (0%)	4 (2.4%)	0 (0%)	4 (1.4%)	3 (3.0%)
NW Allergy & Asthma	0 (0%)	2 (1.2%)	1 (0.6%)	2 (1.2%)	7 (2.9%)	2 (0.7%)	1 (1.0%)
Kaiser	1 (1.8%)	3 (1.9%)	0 (0%)	5 (3.0%)	2 (0.8%)	1 (0.4%)	2 (2.0%)
Katmai Oncology Group	2 (3.5%)	3 (1.9%)	1 (0.6%)	1 (0.6%)	0 (0%)	3 (1.1%)	4 (4.0%)
North Star Lodge	1 (1.8%)	4 (2.5%)	2 (1.3%)	2 (1.2%)	2 (0.8%)	2 (0.7%)	1 (1.0%)
UWNC	0 (0%)	2 (1.2%)	2 (1.3%)	2 (1.2%)	3 (1.3%)	2 (0.7%)	2 (2.0%)
Alaska Native Medical Center	1 (1.8%)	0 (0%)	1 (0.6%)	3 (1.8%)	3 (1.3%)	3 (1.1%)	0 (0%)
Madigan	1 (1.8%)	4 (2.5%)	0 (0%)	0 (0%)	5 (2.1%)	1 (0.4%)	0 (0%)
Jefferson Healthcare Oncology Clini	0 (0%)	2 (1.2%)	1 (0.6%)	1 (0.6%)	2 (0.8%)	3 (1.1%)	0 (0%)
Queen's cancer center	0 (0%)	2 (1.2%)	1 (0.6%)	0 (0%)	37 (28%)	47 (27%)	29 (19%)	47 (23%)	55 (22%)	74 (29%)
Other	25 (19%)	32 (19%)	22 (15%)	35 (17%)	38 (15%)	26 (10%)
Multicare	4 (3.0%)	11 (6.4%)	12 (8.1%)	7 (3.4%)	17 (6.7%)	21 (8.3%)
Northwest Medical Specialties	5 (3.8%)	9 (5.2%)	13 (8.7%)	7 (3.4%)	12 (4.7%)	14 (5.5%)
Providence	4 (3.0%)	4 (2.3%)	7 (4.7%)	16 (7.8%)	15 (5.9%)	14 (5.5%)
Valley Med Center	5 (3.8%)	10 (5.8%)	5 (3.4%)	5 (2.4%)	11 (4.3%)	11 (4.3%)
Swedish	7 (5.3%)	4 (2.3%)	2 (1.3%)	8 (3.9%)	9 (3.5%)	2 (0.8%)
Confluence Health	5 (3.8%)	6 (3.5%)	2 (1.3%)	7 (3.4%)	5 (2.0%)	5 (2.0%)
Kadlec Clinic	3 (2.3%)	4 (2.3%)	10 (6.7%)	2 (1.0%)	5 (2.0%)	6 (2.4%)
Virginia Mason	1 (0.8%)	3 (1.7%)	2 (1.3%)	9 (4.4%)	9 (3.5%)	5 (2.0%)
Cancer Care Northwest	2 (1.5%)	2 (1.2%)	1 (0.7%)	3 (1.5%)	16 (6.3%)	4 (1.6%)
Alaska Oncology & Hematology	0 (0%)	2 (1.2%)	3 (2.0%)	9 (4.4%)	7 (2.7%)	4 (1.6%)
Peace Health	2 (1.5%)	1 (0.6%)	8 (5.4%)	4 (2.0%)	4 (1.6%)	6 (2.4%)
Everett Clinic	0 (0%)	4 (2.3%)	5 (3.4%)	2 (1.0%)	11 (4.3%)	2 (0.8%)
Skagit	2 (1.5%)	1 (0.6%)	4 (2.7%)	5 (2.4%)	5 (2.0%)	7 (2.8%)
Kootenai Cancer Center	0 (0%)	4 (2.3%)	6 (4.0%)	4 (2.0%)	3 (1.2%)	5 (2.0%)
Straub Clinic and Hospital	0 (0%)	7 (4.1%)	2 (1.3%)	4 (2.0%)	5 (2.0%)	4 (1.6%)
Vista Oncology	3 (2.3%)	2 (1.2%)	3 (2.0%)	1 (0.5%)	1 (0.4%)	6 (2.4%)
NW Allergy & Asthma	0 (0%)	3 (1.7%)	1 (0.7%)	7 (3.4%)	1 (0.4%)	3 (1.2%)
Kaiser	4 (3.0%)	0 (0%)	0 (0%)	6 (2.9%)	2 (0.8%)	2 (0.8%)
Katmai Oncology Group	3 (2.3%)	3 (1.7%)	1 (0.7%)	0 (0%)	0 (0%)	7 (2.8%)
North Star Lodge	5 (3.8%)	1 (0.6%)	2 (1.3%)	2 (1.0%)	1 (0.4%)	3 (1.2%)
UWNC	1 (0.8%)	2 (1.2%)	3 (2.0%)	1 (0.5%)	4 (1.6%)	2 (0.8%)
Alaska Native Medical Center	1 (0.8%)	0 (0%)	2 (1.3%)	4 (2.0%)	3 (1.2%)	1 (0.4%)
Madigan	4 (3.0%)	1 (0.6%)	0 (0%)	3 (1.5%)	3 (1.2%)	0 (0%)
Jefferson Healthcare Oncology Clini	1 (0.8%)	1 (0.6%)	2 (1.3%)	0 (0%)	4 (1.6%)	1 (0.4%)
Queen's cancer center	2 (1.5%)	1 (0.6%)	0 (0%)	0 (0%)	1 (0.4%)	5 (2.0%)
St Michael	3 (2.3%)	3 (1.7%)	0 (0%)	1 (0.5%)	0 (0%)	2 (0.8%)
VA	1 (0.8%)	0 (0%)	0 (0%)	2 (1.0%)	1 (0.4%)	4 (1.6%)
Rockwood Cancer and Blood Specialty	1 (0.8%)	3 (1.7%)	1 (0.7%)	1 (0.5%)	0 (0%)	3 (1.1%)	3 (3.0%)
St Michael	1 (1.8%)	3 (1.9%)	2 (1.3%)	0 (0%)	1 (0.4%)	2 (0.7%)	0 (0%)
VA	0 (0%)	1 (0.6%)	0 (0%)	1 (0.6%)	1 (0.4%)	4 (1.4%)	1 (1.0%)
Rockwood Cancer and Blood Specialty	0 (0%)	2 (1.2%)	2 (1.3%)	1 (0.6%)	1 (0.4%)	0 (0%)	1 (1.0%)
Logan Health Hematology & Oncology	0 (0%)	0 (0%)	1 (0.6%)	0 (0%)	0 (0%)	5 (1.8%)	0 (0%)
Northwest Oncology and Hematology	1 (1.8%)	1 (0.6%)	1 (0.6%)	0 (0%)	2 (0.8%)	1 (0.4%)	0 (0%)
1 (0.7%)	0 (0%)	3 (1.2%)	2 (0.8%)
Northwest Oncology and Hematology	2 (1.5%)	1 (0.6%)	0 (0%)	2 (1.0%)	1 (0.4%)	0 (0%)
Compass Oncology	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (1.4%)	0 (0%)
4 (1.6%)
Hawaii Cancer Care Waterfront Plaza	0 (0%)	0 (0%)	0 (0%)	1 (0.6%)	2 (0.8%)	1 (0.4%)	0 (0%)
Unknown	26	60	48	58	44	36	32
1 (0.5%)	3 (1.2%)	0 (0%)
Unknown	59	56	49	50	35	55
1 n (%)

Table of insurance companies

Show code

theme_gtsummary_compact()

db %>%
  transmute(
    "Rx year" = Tx.year,
    "Insurance company" = PAYOR_NAME,
    Insurance = FINANCIAL_CLASS
  ) %>%
  tbl_summary(
    by = "Rx year",
    missing = "ifany",
    sort = all_categorical() ~ "frequency",
    statistic = list(
      all_continuous() ~ "{median} ({p25}, {p75})",
      all_categorical() ~ "{n} ({p}%)"
    )
  )%>%
  bold_labels()

>>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622

Characteristic	2018, N = 1921	2019, N = 2281	2020, N = 1981	2021, N = 2551	2022, N = 2901	2023, N = 3081
Insurance company
HB/PB BDCT	55 (29%)	80 (35%)	42 (21%)	57 (22%)	65 (22%)	78 (25%)
MEDICARE	50 (26%)	33 (14%)	44 (22%)	62 (24%)	56 (19%)	64 (21%)
HB/PB URN - OPTUM HEALTH	12 (6.3%)	18 (7.9%)	20 (10%)	18 (7.1%)	24 (8.3%)	17 (5.5%)
MOLINA HEALTHCARE MEDICAID	9 (4.7%)	11 (4.8%)	17 (8.6%)	12 (4.7%)	15 (5.2%)	18 (5.8%)
AETNA US HEALTHCARE	12 (6.3%)	11 (4.8%)	7 (3.5%)	14 (5.5%)	18 (6.2%)	14 (4.5%)
PREMERA BLUE CROSS	9 (4.7%)	11 (4.8%)	2 (1.0%)	7 (2.8%)	26 (9.0%)	11 (3.6%)
HEALTH NET FEDERAL SERVICES TRICARE	1 (0.5%)	3 (1.3%)	9 (4.5%)	11 (4.3%)	5 (1.7%)	4 (1.3%)
WA MEDICAID	2 (1.0%)	5 (2.2%)	3 (1.5%)	7 (2.8%)	8 (2.8%)	5 (1.6%)
KAISER FHP OF WA (GROUP HEALTH)	7 (3.6%)	3 (1.3%)	2 (1.0%)	3 (1.2%)	4 (1.4%)	9 (2.9%)
BCBS OUT OF AREA	1 (0.5%)	3 (1.3%)	2 (1.0%)	5 (2.0%)	4 (1.4%)	10 (3.2%)
GENERIC/UNLISTED	4 (2.1%)	8 (3.5%)	5 (2.5%)	1 (0.4%)	0 (0%)	2 (0.6%)
HB/PB INTERLINK HEALTH SERVICES	4 (2.1%)	2 (0.9%)	1 (0.5%)	5 (2.0%)	1 (0.3%)	6 (1.9%)
COMMUNITY HEALTH PLAN OF WA MEDICAID	3 (1.6%)	1 (0.4%)	5 (2.5%)	0 (0%)	4 (1.4%)	3 (1.0%)
GRANTS AND BUDGETS	1 (0.5%)	3 (1.3%)	4 (2.0%)	7 (2.8%)	1 (0.3%)	0 (0%)
REGENCE BLUE SHIELD	1 (0.5%)	2 (0.9%)	3 (1.5%)	2 (0.8%)	2 (0.7%)	6 (1.9%)
COORDINATED CARE CORPORATION MEDICAID	3 (1.6%)	1 (0.4%)	2 (1.0%)	3 (1.2%)	3 (1.0%)	3 (1.0%)
OPTUM HEALTHCARE SOLUTIONS/URN	2 (1.0%)	1 (0.4%)	2 (1.0%)	0 (0%)	4 (1.4%)	6 (1.9%)
BCBS OF ILLINOIS - BOEING	1 (0.5%)	4 (1.8%)	0 (0%)	2 (0.8%)	2 (0.7%)	5 (1.6%)
WELLPOINT MEDICAID	2 (1.0%)	5 (2.2%)	1 (0.5%)	0 (0%)	5 (1.7%)	1 (0.3%)
ALASKA MEDICAID	1 (0.5%)	0 (0%)	2 (1.0%)	3 (1.2%)	2 (0.7%)	3 (1.0%)
CIGNA	0 (0%)	2 (0.9%)	2 (1.0%)	5 (2.0%)	0 (0%)	2 (0.6%)
TRIWEST HEALTHCARE ALLIANCE	1 (0.5%)	1 (0.4%)	0 (0%)	2 (0.8%)	1 (0.3%)	6 (1.9%)
HUMANA MEDICARE	0 (0%)	0 (0%)	3 (1.5%)	2 (0.8%)	4 (1.4%)	1 (0.3%)
AETNA MEDICARE	0 (0%)	1 (0.4%)	0 (0%)	1 (0.4%)	2 (0.7%)	5 (1.6%)
HB/PB LIFETRAC	1 (0.5%)	2 (0.9%)	2 (1.0%)	2 (0.8%)	1 (0.3%)	1 (0.3%)
REGENCE BLUE SHIELD MEDICARE	0 (0%)	1 (0.4%)	0 (0%)	3 (1.2%)	2 (0.7%)	3 (1.0%)
HEALTHCARE MGMT ADMIN	0 (0%)	1 (0.4%)	1 (0.5%)	3 (1.2%)	2 (0.7%)	0 (0%)
FHCC CONTRACTS	0 (0%)	2 (0.9%)	2 (1.0%)	1 (0.4%)	1 (0.3%)	0 (0%)
KAISER FHP OF WA MEDICARE (GROUP HEALTH)	0 (0%)	1 (0.4%)	3 (1.5%)	0 (0%)	0 (0%)	2 (0.6%)
MERITAIN HEALTH AETNA	1 (0.5%)	1 (0.4%)	0 (0%)	2 (0.8%)	2 (0.7%)	0 (0%)
MOLINA HEALTHCARE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	5 (1.7%)	1 (0.3%)
MONTANA MEDICAID	0 (0%)	1 (0.4%)	1 (0.5%)	0 (0%)	1 (0.3%)	3 (1.0%)
REGENCE UMP	1 (0.5%)	0 (0%)	0 (0%)	1 (0.4%)	2 (0.7%)	2 (0.6%)
UNITED HEALTHCARE MEDICAID	1 (0.5%)	0 (0%)	1 (0.5%)	1 (0.4%)	2 (0.7%)	1 (0.3%)
ZENITH AMERICAN SOLUTIONS AETNA	0 (0%)	1 (0.4%)	2 (1.0%)	2 (0.8%)	0 (0%)	1 (0.3%)
BCBS OUT OF AREA MEDICARE	0 (0%)	0 (0%)	2 (1.0%)	1 (0.4%)	2 (0.7%)	0 (0%)
DEPT OF LABOR AND INDUSTRIES	0 (0%)	2 (0.9%)	0 (0%)	0 (0%)	0 (0%)	2 (0.6%)
FEP	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	3 (1.0%)	0 (0%)
KAISER FHP OF WA CORE-HIX (GROUP HEALTH)	1 (0.5%)	2 (0.9%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)
PREMERA BLUE CROSS MEDICARE	0 (0%)	0 (0%)	1 (0.5%)	2 (0.8%)	1 (0.3%)	0 (0%)
US DEPARTMENT OF LABOR	1 (0.5%)	1 (0.4%)	0 (0%)	0 (0%)	0 (0%)	2 (0.6%)
FINANCIAL ASSISTANCE	0 (0%)	0 (0%)	2 (1.0%)	0 (0%)	0 (0%)	1 (0.3%)
MOLINA HEALTHCARE MEDICARE	0 (0%)	1 (0.4%)	0 (0%)	1 (0.4%)	0 (0%)	1 (0.3%)
OPTUM CARE NETWORK MEDICARE	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	1 (0.3%)	1 (0.3%)
ASURIS NORTHWEST HEALTH	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	1 (0.3%)
BRITISH COLUMBIA	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	1 (0.3%)
FHCC HOLDING FUNDS	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	1 (0.3%)
IDAHO MEDICAID	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)
INTERLINK	0 (0%)	0 (0%)	1 (0.5%)	0 (0%)	0 (0%)	1 (0.3%)
MEDICARE ADVANTAGE GENERIC	1 (0.5%)	1 (0.4%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
PACIFICSOURCE HEALTH PLANS	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	1 (0.3%)
UNITED HEALTHCARE	0 (0%)	1 (0.4%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
BCBS OUT OF AREA GENERIC	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)
BCBS OUT OF AREA MEDICAID	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)
COMMUNITY HEALTH PLAN OF WA MEDICARE	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
FIRST CHOICE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)
GEHA	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
ILWU BENEFIT PLANS	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)
LIFEWISE HEALTH PLAN OF WA	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)
SELF INSURED WORKERS COMP OUT OF STATE	0 (0%)	0 (0%)	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)
SELF INSURED WORKERS COMP WA	0 (0%)	0 (0%)	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)
UNITED HEALTHCARE MEDICARE	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
UNITED HEALTHCARE WEST MEDICARE	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
WA STATE HEALTH INS POOL-BMI210	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
WELLCARE OUT OF STATE MEDICARE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)
WELLPOINT MEDICARE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)
Unknown	0	0	0	1	1	0
Insurance
Case Rate	72 (38%)	102 (45%)	65 (33%)	82 (32%)	91 (31%)	102 (33%)
Medicare	53 (28%)	38 (17%)	53 (27%)	74 (29%)	70 (24%)	77 (25%)
Commercial	42 (22%)	51 (22%)	29 (15%)	51 (20%)	78 (27%)	75 (24%)
Medicaid	21 (11%)	25 (11%)	32 (16%)	26 (10%)	41 (14%)	38 (12%)
Other	2 (1.0%)	6 (2.6%)	6 (3.0%)	10 (3.9%)	4 (1.4%)	7 (2.3%)
Tricare	1 (0.5%)	3 (1.3%)	9 (4.5%)	11 (4.3%)	5 (1.7%)	4 (1.3%)
Worker's Comp	1 (0.5%)	3 (1.3%)	2 (1.0%)	0 (0%)	0 (0%)	4 (1.3%)
Self-Pay	0 (0%)	0 (0%)	2 (1.0%)	0 (0%)	0 (0%)	1 (0.3%)
Unknown	0	0	0	1	1	0
1 n (%)

Table of insurance companies

Show code

theme_gtsummary_compact()

db %>%
  transmute(
    "Rx year" = Fiscal.year,
    "Insurance company" = PAYOR_NAME,
    Insurance = FINANCIAL_CLASS
  ) %>%
  tbl_summary(
    by = "Rx year",
    missing = "ifany",
    sort = all_categorical() ~ "frequency",
    statistic = list(
      all_continuous() ~ "{median} ({p25}, {p75})",
      all_categorical() ~ "{n} ({p}%)"
    )
  )%>%
  bold_labels()

Characteristic	2018, N = 831	2019, N = 2221	2020, N = 2061	2021, N = 2231	2022, N = 2831	2023, N = 3211	2024, N = 1331
Insurance company
HB/PB BDCT	26 (31%)	69 (31%)	56 (27%)	57 (26%)	55 (20%)	68 (21%)	46 (35%)
MEDICARE	21 (25%)	50 (23%)	30 (15%)	50 (22%)	70 (25%)	65 (20%)	23 (17%)
HB/PB URN - OPTUM HEALTH	3 (3.6%)	17 (7.7%)	20 (9.7%)	20 (9.0%)	16 (5.7%)	26 (8.1%)	7 (5.3%)
MOLINA HEALTHCARE MEDICAID	5 (6.0%)	7 (3.2%)	18 (8.7%)	9 (4.0%)	18 (6.4%)	21 (6.6%)	4 (3.0%)
AETNA US HEALTHCARE	4 (4.8%)	15 (6.8%)	5 (2.4%)	19 (8.5%)	13 (4.6%)	12 (3.8%)	8 (6.0%)
PREMERA BLUE CROSS	7 (8.4%)	7 (3.2%)	7 (3.4%)	5 (2.2%)	15 (5.3%)	21 (6.6%)	4 (3.0%)
HEALTH NET FEDERAL SERVICES TRICARE	0 (0%)	4 (1.8%)	5 (2.4%)	9 (4.0%)	7 (2.5%)	8 (2.5%)	0 (0%)
WA MEDICAID	0 (0%)	6 (2.7%)	3 (1.5%)	5 (2.2%)	6 (2.1%)	7 (2.2%)	3 (2.3%)
KAISER FHP OF WA (GROUP HEALTH)	5 (6.0%)	2 (0.9%)	5 (2.4%)	2 (0.9%)	2 (0.7%)	8 (2.5%)	4 (3.0%)
BCBS OUT OF AREA	1 (1.2%)	1 (0.5%)	2 (1.0%)	5 (2.2%)	4 (1.4%)	8 (2.5%)	4 (3.0%)
GENERIC/UNLISTED	1 (1.2%)	7 (3.2%)	8 (3.9%)	2 (0.9%)	0 (0%)	2 (0.6%)	0 (0%)
HB/PB INTERLINK HEALTH SERVICES	1 (1.2%)	3 (1.4%)	2 (1.0%)	3 (1.3%)	3 (1.1%)	7 (2.2%)	0 (0%)
COMMUNITY HEALTH PLAN OF WA MEDICAID	0 (0%)	3 (1.4%)	5 (2.4%)	1 (0.4%)	2 (0.7%)	4 (1.3%)	1 (0.8%)
GRANTS AND BUDGETS	0 (0%)	2 (0.9%)	3 (1.5%)	6 (2.7%)	5 (1.8%)	0 (0%)	0 (0%)
REGENCE BLUE SHIELD	0 (0%)	2 (0.9%)	4 (1.9%)	0 (0%)	3 (1.1%)	4 (1.3%)	3 (2.3%)
COORDINATED CARE CORPORATION MEDICAID	1 (1.2%)	3 (1.4%)	1 (0.5%)	1 (0.4%)	4 (1.4%)	4 (1.3%)	1 (0.8%)
OPTUM HEALTHCARE SOLUTIONS/URN	1 (1.2%)	1 (0.5%)	3 (1.5%)	0 (0%)	3 (1.1%)	4 (1.3%)	3 (2.3%)
BCBS OF ILLINOIS - BOEING	0 (0%)	3 (1.4%)	2 (1.0%)	1 (0.4%)	2 (0.7%)	4 (1.3%)	2 (1.5%)
WELLPOINT MEDICAID	0 (0%)	3 (1.4%)	5 (2.4%)	0 (0%)	2 (0.7%)	4 (1.3%)	0 (0%)
ALASKA MEDICAID	1 (1.2%)	0 (0%)	0 (0%)	3 (1.3%)	2 (0.7%)	4 (1.3%)	1 (0.8%)
CIGNA	0 (0%)	1 (0.5%)	2 (1.0%)	3 (1.3%)	3 (1.1%)	1 (0.3%)	1 (0.8%)
TRIWEST HEALTHCARE ALLIANCE	0 (0%)	1 (0.5%)	1 (0.5%)	1 (0.4%)	1 (0.4%)	5 (1.6%)	2 (1.5%)
HUMANA MEDICARE	0 (0%)	0 (0%)	1 (0.5%)	2 (0.9%)	5 (1.8%)	2 (0.6%)	0 (0%)
AETNA MEDICARE	0 (0%)	1 (0.5%)	0 (0%)	1 (0.4%)	2 (0.7%)	1 (0.3%)	4 (3.0%)
HB/PB LIFETRAC	0 (0%)	2 (0.9%)	1 (0.5%)	2 (0.9%)	3 (1.1%)	1 (0.3%)	0 (0%)
REGENCE BLUE SHIELD MEDICARE	0 (0%)	0 (0%)	1 (0.5%)	1 (0.4%)	3 (1.1%)	1 (0.3%)	3 (2.3%)
HEALTHCARE MGMT ADMIN	0 (0%)	1 (0.5%)	0 (0%)	1 (0.4%)	5 (1.8%)	0 (0%)	0 (0%)
FHCC CONTRACTS	0 (0%)	0 (0%)	4 (1.9%)	0 (0%)	1 (0.4%)	1 (0.3%)	0 (0%)
KAISER FHP OF WA MEDICARE (GROUP HEALTH)	0 (0%)	0 (0%)	4 (1.9%)	0 (0%)	0 (0%)	2 (0.6%)	0 (0%)
MERITAIN HEALTH AETNA	1 (1.2%)	1 (0.5%)	0 (0%)	2 (0.9%)	1 (0.4%)	1 (0.3%)	0 (0%)
MOLINA HEALTHCARE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	4 (1.4%)	2 (0.6%)	0 (0%)
MONTANA MEDICAID	0 (0%)	1 (0.5%)	0 (0%)	1 (0.4%)	0 (0%)	4 (1.3%)	0 (0%)
REGENCE UMP	1 (1.2%)	0 (0%)	0 (0%)	1 (0.4%)	2 (0.7%)	2 (0.6%)	0 (0%)
UNITED HEALTHCARE MEDICAID	0 (0%)	1 (0.5%)	1 (0.5%)	0 (0%)	1 (0.4%)	2 (0.6%)	1 (0.8%)
ZENITH AMERICAN SOLUTIONS AETNA	0 (0%)	0 (0%)	1 (0.5%)	2 (0.9%)	2 (0.7%)	0 (0%)	1 (0.8%)
BCBS OUT OF AREA MEDICARE	0 (0%)	0 (0%)	0 (0%)	2 (0.9%)	1 (0.4%)	2 (0.6%)	0 (0%)
DEPT OF LABOR AND INDUSTRIES	0 (0%)	1 (0.5%)	1 (0.5%)	0 (0%)	0 (0%)	1 (0.3%)	1 (0.8%)
FEP	0 (0%)	1 (0.5%)	0 (0%)	0 (0%)	2 (0.7%)	1 (0.3%)	0 (0%)
KAISER FHP OF WA CORE-HIX (GROUP HEALTH)	1 (1.2%)	1 (0.5%)	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	1 (0.8%)
PREMERA BLUE CROSS MEDICARE	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	3 (1.1%)	0 (0%)	0 (0%)
US DEPARTMENT OF LABOR	1 (1.2%)	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	1 (0.8%)
FINANCIAL ASSISTANCE	0 (0%)	0 (0%)	1 (0.5%)	1 (0.4%)	0 (0%)	1 (0.3%)	0 (0%)
MOLINA HEALTHCARE MEDICARE	0 (0%)	0 (0%)	1 (0.5%)	0 (0%)	1 (0.4%)	1 (0.3%)	0 (0%)
OPTUM CARE NETWORK MEDICARE	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	1 (0.4%)	0 (0%)	1 (0.8%)
ASURIS NORTHWEST HEALTH	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	2 (0.6%)	0 (0%)
BRITISH COLUMBIA	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	1 (0.3%)	0 (0%)
FHCC HOLDING FUNDS	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	1 (0.3%)	0 (0%)
IDAHO MEDICAID	0 (0%)	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.8%)
INTERLINK	0 (0%)	0 (0%)	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	1 (0.8%)
MEDICARE ADVANTAGE GENERIC	0 (0%)	1 (0.5%)	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
PACIFICSOURCE HEALTH PLANS	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	1 (0.3%)	0 (0%)
UNITED HEALTHCARE	0 (0%)	1 (0.5%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
BCBS OUT OF AREA GENERIC	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)
BCBS OUT OF AREA MEDICAID	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
COMMUNITY HEALTH PLAN OF WA MEDICARE	0 (0%)	1 (0.5%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
FIRST CHOICE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
GEHA	1 (1.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
ILWU BENEFIT PLANS	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.8%)
LIFEWISE HEALTH PLAN OF WA	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
SELF INSURED WORKERS COMP OUT OF STATE	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)	0 (0%)
SELF INSURED WORKERS COMP WA	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)	0 (0%)
UNITED HEALTHCARE MEDICARE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
UNITED HEALTHCARE WEST MEDICARE	1 (1.2%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
WA STATE HEALTH INS POOL-BMI210	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
WELLCARE OUT OF STATE MEDICARE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.3%)	0 (0%)
WELLPOINT MEDICARE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.4%)	0 (0%)	0 (0%)
Unknown	0	0	0	0	1	1	0
Insurance
Case Rate	30 (36%)	91 (41%)	79 (38%)	82 (37%)	77 (27%)	102 (32%)	53 (40%)
Medicare	22 (27%)	53 (24%)	38 (18%)	58 (26%)	88 (31%)	75 (23%)	31 (23%)
Commercial	23 (28%)	44 (20%)	41 (20%)	44 (20%)	66 (23%)	75 (23%)	33 (25%)
Medicaid	7 (8.4%)	25 (11%)	33 (16%)	20 (9.0%)	36 (13%)	50 (16%)	12 (9.0%)
Other	0 (0%)	3 (1.4%)	8 (3.9%)	7 (3.1%)	8 (2.8%)	7 (2.2%)	2 (1.5%)
Tricare	0 (0%)	4 (1.8%)	5 (2.4%)	9 (4.0%)	7 (2.5%)	8 (2.5%)	0 (0%)
Worker's Comp	1 (1.2%)	2 (0.9%)	1 (0.5%)	2 (0.9%)	0 (0%)	2 (0.6%)	2 (1.5%)
Self-Pay	0 (0%)	0 (0%)	1 (0.5%)	1 (0.4%)	0 (0%)	1 (0.3%)	0 (0%)
Unknown	0	0	0	0	1	1	0
1 n (%)